How Technology Helps Measure Soccer Statistics and Tactics

Over the last 5-6 months, I have had different conversations with people who are working on technologies related to soccer. And I recall writing about Tactical Analysis in Soccer for SportTechie some years back and thought I will reshare it here plus add a bit of update as some things have changed since then.

What are some of the Football Tactical Analysis Websites?

Squawka is a tactical analysis web application that provides a platform where one can view real-time and post-match statistics of (almost) everything that goes on in a football match. This includes time of possession, number of passes, number of shots, shot accuracy, chances created, tackles (or duels), blocks (or defensive actions), player stats etc. Match analysis data is presented in a field diagram with coloured dots and lines and heat maps. Those statistics can then be filtered by team and player for various types of analysis – whether it’s comparing player performance or looking at shots from one team or overall team events. A useful feature is the timeline scrolling which allows one to look at specific 5 min blocks of match activity.

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Player Performance Comparison

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Shots – where they were taken and where they landed

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Events Heat Maps

The comparison matrix is another interesting tool on the site that looks at stats over an entire season. For example, one can select five different teams in the 2013/2014 season of the Australian League and compare stats that they are interested in. The stats displayed can be filtered by ‘total for the season’, ‘average per game’ or ‘per 90 metrics’. One can also compare teams from different leagues and different seasons.  

A special metric of this website is the Squawka Player Performance Score which is calculated using a large amount of data. This player performance score is broken down into “attack”, “defence” and “possession” statistics.

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Comparison Matrix Example

Four Four Two also provides match analysis data on their website in their Stats Zone section. It pretty much provides the same data available in Squawka, except the information is presented in a slightly different way and they don’t have a player performance score. The  Stats Zone allows all the match activities of a player to be viewed together in the Overall-player dashboard, instead of having to select the individual events in the Squawka dashboard. {Update: unfortunately FourFourTwo has discontinued Stats Zone due to limited resources}

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Stats Zone and what their Summary Statistics used to look like

Another site called Outside of the Boot writes commentaries and analysis on selected matches and supports their analysis using statistical data from the above two websites. Other than breaking down the statistics and what was going on in each match, they give reviews on the general style and tactics of a team, player or coach.

Where and how are the data collected?

Regardless of how the data is presented, what’s important is the reliability of the data and where their source is. Interestingly, both sites get their data from Opta, a sports data company that collect, package, analyse and distribute live data. Opta briefly explains on their website that their data collection process is labour intensive with three performance analysts assigned to each match; with one collecting all of the home team actions, one doing the away team and a third analyst checking the data for consistency and adding additional layers of data. They then run a full post-match check within 48 hours to ensure that the database is as accurate as possible.

But what exactly does each analyst do? By using their proprietary software, each Opta analyst puts in the live video feed of a match, then by using hotkeys, every activity that involves the ball is “tagged” – this “tagging” or tracking will record the time each activity started and ended and the X-Y coordinates of the start and end positions. For those who have used video analysis software like SportsCode (now owned by Hudl) or Dartfish, this will sound familiar. But what Opta has done is standardize their activity definition and tracking methods, so every analyst is trained to tag or code the exact same way. This means consistency in the data, allowing every match and every player to be compared using the same standards. The cool thing is, by feeding in historical matches (like all the past world cup matches), they can compare the performance of players from different decades. Check out this video that talks a bit more about what Opta Sports do:

Are there alternative (automated) technologies?

There are a number of athlete tracking technology out there that are either based on wearable technology (Catapult Sports, Tracktics, Polar Team, STATSports & SPT etc) or camera and image processing technology (Stats SportVU and TRACAB). The advantage of wearable sensors is that they can accurately track each athlete’s acceleration and impacts (in three axes) and some even track the players’ heart rate – something that is not possible with any of the current camera or video technology. But data from wearable sensors typically belong to the teams and not shared unless there is an arrangement with broadcasters. With Stats, they claim to not only track real-time 2D (X-Y) positioning data of the ball and the players, but its complex algorithms can also analyse and work out information like speeds, distances, possessions, passings, defence stats and turnovers. So technically, automating tactical analysis to some extent is possible but how much information can be made publicly available is another question.

Some final thoughts

The technology and methods used in Tactical Analysis in football have become more widespread over the years (and its still growing). The statistics that are made available can not only give punters additional information for betting, it can add new dimensions to watching each game. It provides viewers with a better understanding of what the players are actually doing (individually and as a team) and how they have been performing over a season with an unbiased quantified evaluation. For coaches and team managers, it means their decisions (in terms of training, strategising or even talent identification) don’t have to rely too much on gut feel but can be supported with numbers. How much they want to trust those numbers is another thing altogether.

If you know an exceptional app or technology in tactical analysis that is not mentioned here or maybe its still in development, feel free to leave a comment about it, and finally, thanks for reading!


Here are a few other related articles and blogs for those who like geek out a bit more on the topic:

  • Different Game: https://differentgame.wordpress.com/
  • Paper on big data and tactical analysis in elite soccer: link
  • Paper on tactical analysis using pattern recognition: link
  • Paper on a new tactical metric that looks at effective play: link

Some thoughts and takeaways from #SAC16

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The 2016 Asia-Pacific Sports Analytics Conference took place recently at the NAB Village. Its only the second time this conference is held and I have to say it has done really well. The numbers prove it – 865 attendees (according to the Whova app), 33 sessions that ran concurrently in 3 different rooms, 45 Speakers (all experts in their fields) representing 57 organisations, and 12 startups that pitched their innovative ideas/products/services.  There was even a waitlist 2 weeks before the event. This goes to show the growing booming popularity of data analytics, and the potential impact it could have on the different aspects of sports.

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You know it’s a serious conference when it has its own coffee cup

Unfortunately, as with any great conference where there are sessions running at the same time, people would be torn between 2 (or possibly 3) presentations they are keen to attend. Fortunately, from what I heard, videos of all the sessions will be uploaded in a few weeks and we will be able to catch up with every single one that we missed. Just keep a lookout on the conference website here. In the meantime, here are some of my takeaways from the few sessions I managed to attend.

Smart equipment:

Professor Tino Fuss presented some of the research and development that was going on at RMIT including a smart cricket ball, a smart soccer boot and smart compression garment. With the advancement of inertia sensing microtechnology and novel pressure sensing technology, sensors can be placed unobtrusively on the athlete and equipment, measuring a range of parameters at much higher magnitudes. No doubt that the sensor data that’s acquired has to be analysed to solve a problem or confirm a hypothesis. That’s where analytics play an important role. But applying the appropriate sensor technology does open up opportunities to analyse new parameters like the sweet-spot on a soccer boot that increases the chance of a goal.

Wearable tech for rehab:

Shireen Mansoori is a doctor in physical therapy who applies wearable technology in her practice with elite athletes. She presented a model where she combined physiotherapy and data analytics for athlete optimisation. She uses Catapult units for monitoring an athlete’s Player Load & Hi Deceleration efforts to find trends that lead to injury. But she also uses other wearable tracking devices such as the Misfit shine on the athletes, health/wellness monitoring apps, and an athlete sleep screening questionnaire to monitor an athlete’s sleep and daily activities. Having other forms of data paints a much clearer picture of what an athlete is going through, and allows her to find out why the athlete is recovering faster or performing below expectations.

Video analysis & Artificial intelligence:

In cases where it is still obtrusive to place sensors on athletes (for example in swimming competitions); or where wearable sensors can’t provide specific activity/events information (for example attack, pass or steal events in hockey), sports analysts turn to video analysis/coding. However, much of the video analysis work involves a sports scientist (or two) manually tagging/coding every event during the competition. Stuart Morgan, sports analyst at AIS, talked about developing computer vision algorithms to  detect patterns and features and somehow automate the tagging. But this approach (human engineered method) has lots of limitations including it being non-transferrable and not very adaptable (for use in different sports). So AIS is collaborating with researchers at La Trobe Uni to apply deep learning (using Convolutional Neural Networks) to process the video images and work out whats happening. The advantage of deep learning is that it’s adaptable and it automatically creates new features. It still has some way to go as it’s not error free and users can’t really tell what logic led to the decisions.

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Stuart Morgan talking about AI in sports analytics

From elite to grassroots:

Most of the stuff mentioned above happens in the professional/elite athlete space. However there is also an increased trend of sports tech/analytics companies developing products for athletes and coaches who participate in their local leagues. Hudl‘s video analysis software was first developed for professional teams. But today, their software caters to high school teams and their requirements. They have developed mobile apps that allows video recording and editing directly from the coaches’ mobile device, and there’s even a platform for sharing videos and facilitating talent identification.

Athlete tracking wearables have also moved in the same direction. Startup companies like Essential GPS and Sports Performance Tracking have developed more affordable tracking solutions so that teams with lower budgets can also track and monitor their players. Although it seems to be purely GPS data (without motion data), and only post game/training analysis (not real-time), it is still a good start. Or maybe a simplified, cost reduced system is all that is required?

From the startup community:

So there were 12 startups showcased in the conference. Other than the 2 mentioned above, there were 4 other startups that have built hardware in areas of performance tracking, drone racing, rehabilitation, and custom protective gear. The others were mainly software based, providing services and platforms in media, news, sales, marketing, VR and team management. They have all developed solutions hoping to fill a gap identified in the sports industry. Personally I am just amazed at some of the novelty and innovation they have come up with; and as this blog post says it, they are all innovators.

Bottom line:

I think what sums up this conference for me is that sports analytics is all about adapting and innovating. Everyone in their own ways are trying to fix a problem (or come up with a better solution) or improve work flow or even create new opportunities (e.g. esports and fantasy league). But the process is never a straight line from point A to B. The solutions need to be adapting over and over (almost like deep learning). Sometimes there needs to be collaborations and sometimes the end solution needs to be a combination of solutions. Whichever the case, iterate the process as quickly as possible till an optimum outcome is reached.

The”one-size-fits-all” solution doesn’t work very well anymore and mass customisation is becoming the norm. As mentioned by John Eren MP and Laura Anderson during their welcome addresses, we are slowly moving away from economies of scale and towards economies of scope.

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Group photo after welcome address. From John Eren Mp’s facebook page (link)

Anyway, congrats again to PSCL and KPMG for another successful event and thanks for reading!